{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 基于pandas的数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "#图片直接画在网页里面\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 线形图的画法\n",
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('2000/1/1', periods=1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01    -0.885214\n",
       "2000-01-02    -0.003865\n",
       "2000-01-03    -0.098502\n",
       "2000-01-04     1.018444\n",
       "2000-01-05     1.475170\n",
       "2000-01-06    -0.019528\n",
       "2000-01-07     0.151470\n",
       "2000-01-08    -0.543431\n",
       "2000-01-09    -0.270579\n",
       "2000-01-10     0.677821\n",
       "2000-01-11     2.771281\n",
       "2000-01-12     3.649866\n",
       "2000-01-13     4.001163\n",
       "2000-01-14     3.207959\n",
       "2000-01-15     2.429311\n",
       "2000-01-16     3.125629\n",
       "2000-01-17     4.072247\n",
       "2000-01-18     4.802534\n",
       "2000-01-19     5.136990\n",
       "2000-01-20     5.509247\n",
       "2000-01-21     5.827376\n",
       "2000-01-22     4.863966\n",
       "2000-01-23     4.831006\n",
       "2000-01-24     2.019943\n",
       "2000-01-25     0.371737\n",
       "2000-01-26     1.384343\n",
       "2000-01-27     0.603847\n",
       "2000-01-28    -0.404607\n",
       "2000-01-29     1.263749\n",
       "2000-01-30     0.128345\n",
       "                ...    \n",
       "2002-08-28   -63.312920\n",
       "2002-08-29   -65.043494\n",
       "2002-08-30   -65.145546\n",
       "2002-08-31   -66.784407\n",
       "2002-09-01   -68.290305\n",
       "2002-09-02   -68.613165\n",
       "2002-09-03   -68.720295\n",
       "2002-09-04   -68.935542\n",
       "2002-09-05   -68.105567\n",
       "2002-09-06   -68.080736\n",
       "2002-09-07   -68.752096\n",
       "2002-09-08   -69.159858\n",
       "2002-09-09   -69.514502\n",
       "2002-09-10   -69.466207\n",
       "2002-09-11   -67.837047\n",
       "2002-09-12   -69.787665\n",
       "2002-09-13   -71.290211\n",
       "2002-09-14   -72.961594\n",
       "2002-09-15   -70.810578\n",
       "2002-09-16   -71.428329\n",
       "2002-09-17   -70.618216\n",
       "2002-09-18   -70.616647\n",
       "2002-09-19   -69.888615\n",
       "2002-09-20   -70.383523\n",
       "2002-09-21   -71.951253\n",
       "2002-09-22   -72.657505\n",
       "2002-09-23   -73.577732\n",
       "2002-09-24   -75.833383\n",
       "2002-09-25   -75.135737\n",
       "2002-09-26   -75.490288\n",
       "Freq: D, Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = ts.cumsum() # 进行累积求和,全部加起来为1000\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean      -13.308516\n",
       "std        22.876900\n",
       "min       -75.833383\n",
       "25%       -24.654504\n",
       "50%        -5.526650\n",
       "75%         2.025071\n",
       "max        20.939956\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e7f518>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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X+nB/TxBFSeoG7Mq4k264u7FFtGv92Wd1Sb1gSkq0S/2773TedkYG8Mc/6pSt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\nyAMYsImIiDyAAZuIiMgDGLCJiIg84P8ArstfCJTr6xwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f95748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot(title='cumsum', style='r-', figsize=(8, 6))#尺寸为英寸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-17.514972</td>\n",
       "      <td>16.656293</td>\n",
       "      <td>-18.725358</td>\n",
       "      <td>1.719392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.925406</td>\n",
       "      <td>14.046021</td>\n",
       "      <td>15.527622</td>\n",
       "      <td>7.727402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-39.847011</td>\n",
       "      <td>-15.696090</td>\n",
       "      <td>-45.936371</td>\n",
       "      <td>-9.848659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-30.411148</td>\n",
       "      <td>7.975227</td>\n",
       "      <td>-34.287405</td>\n",
       "      <td>-3.774240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-16.188689</td>\n",
       "      <td>16.140076</td>\n",
       "      <td>-16.040494</td>\n",
       "      <td>0.092359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-6.261588</td>\n",
       "      <td>27.207313</td>\n",
       "      <td>-5.835948</td>\n",
       "      <td>4.039049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.885197</td>\n",
       "      <td>47.422336</td>\n",
       "      <td>9.422126</td>\n",
       "      <td>24.830878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A            B            C            D\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean    -17.514972    16.656293   -18.725358     1.719392\n",
       "std      12.925406    14.046021    15.527622     7.727402\n",
       "min     -39.847011   -15.696090   -45.936371    -9.848659\n",
       "25%     -30.411148     7.975227   -34.287405    -3.774240\n",
       "50%     -16.188689    16.140076   -16.040494     0.092359\n",
       "75%      -6.261588    27.207313    -5.835948     4.039049\n",
       "max       9.885197    47.422336     9.422126    24.830878"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))\n",
    "df = df.cumsum()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb07ea90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OYnLYUAQDbrb12dvjj3cmoI7wPUCr1dK/f3969+5Nv379mDdvHlar53UkVc4wynKUQtOL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\nV5M2eUrV8bDrryd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+rZRzDLniCvwSeziV4DPlZDdeJ08TjMfT8WnfvtXo2jeUFvnpVu93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\nRCJpJ0iDL5FIJO0EafAlEomknfD/AZvxg4ccMYcHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x55797b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000B463828>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000B659860>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000B66B438>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000B727438>], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GpdTLAFrrrcCHwDZgEXCDeAYJHY1rpvfjlpmDOGec+UqzJqYrKq9i+pPLKauU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\nlECKrQRaN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dAnlgcpxohKID/cXGXF2rQn0NJsmpVseo/QYIeWrVvB4cBVOpDmLV+ZK1tx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\nVgI2NjY2KYytBGxsbGxSmP8PX8QkHCtGDZAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98bcb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(subplots=True, figsize=(6,12))# 分成四个子图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-17.514972</td>\n",
       "      <td>16.656293</td>\n",
       "      <td>-18.725358</td>\n",
       "      <td>1.719392</td>\n",
       "      <td>499.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.925406</td>\n",
       "      <td>14.046021</td>\n",
       "      <td>15.527622</td>\n",
       "      <td>7.727402</td>\n",
       "      <td>288.819436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-39.847011</td>\n",
       "      <td>-15.696090</td>\n",
       "      <td>-45.936371</td>\n",
       "      <td>-9.848659</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-30.411148</td>\n",
       "      <td>7.975227</td>\n",
       "      <td>-34.287405</td>\n",
       "      <td>-3.774240</td>\n",
       "      <td>249.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-16.188689</td>\n",
       "      <td>16.140076</td>\n",
       "      <td>-16.040494</td>\n",
       "      <td>0.092359</td>\n",
       "      <td>499.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-6.261588</td>\n",
       "      <td>27.207313</td>\n",
       "      <td>-5.835948</td>\n",
       "      <td>4.039049</td>\n",
       "      <td>749.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.885197</td>\n",
       "      <td>47.422336</td>\n",
       "      <td>9.422126</td>\n",
       "      <td>24.830878</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A            B            C            D           ID\n",
       "count  1000.000000  1000.000000  1000.000000  1000.000000  1000.000000\n",
       "mean    -17.514972    16.656293   -18.725358     1.719392   499.500000\n",
       "std      12.925406    14.046021    15.527622     7.727402   288.819436\n",
       "min     -39.847011   -15.696090   -45.936371    -9.848659     0.000000\n",
       "25%     -30.411148     7.975227   -34.287405    -3.774240   249.750000\n",
       "50%     -16.188689    16.140076   -16.040494     0.092359   499.500000\n",
       "75%      -6.261588    27.207313    -5.835948     4.039049   749.250000\n",
       "max       9.885197    47.422336     9.422126    24.830878   999.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ID'] = np.arange(len(df))\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbb3f320>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rmg1IjUEikXSd7hYMiVqWWZ6Iqqo+parqQaqqGmVOC01hY54869WbPxHTjjgh8R31iKDc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\nkQy7Vh1W19YOuhyKhoHDJbrK6aW/JR2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0XphKLOXEMaqLms1hVY2GYLj/3AlMGVxAWzBM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\n/wGDhVbihAZz5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb944e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='ID', y=['A', 'C'])# 令x为id，且值画出A,C两列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 柱状图\n",
    "df = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.056052</td>\n",
       "      <td>0.270731</td>\n",
       "      <td>0.501479</td>\n",
       "      <td>0.121491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.770889</td>\n",
       "      <td>0.202760</td>\n",
       "      <td>0.180484</td>\n",
       "      <td>0.993555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.417544</td>\n",
       "      <td>0.766556</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.899541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.040076</td>\n",
       "      <td>0.309443</td>\n",
       "      <td>0.443105</td>\n",
       "      <td>0.238431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.050177</td>\n",
       "      <td>0.261979</td>\n",
       "      <td>0.759423</td>\n",
       "      <td>0.372617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.834048</td>\n",
       "      <td>0.535997</td>\n",
       "      <td>0.057551</td>\n",
       "      <td>0.905784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.971209</td>\n",
       "      <td>0.889872</td>\n",
       "      <td>0.847377</td>\n",
       "      <td>0.687034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.167992</td>\n",
       "      <td>0.380885</td>\n",
       "      <td>0.561302</td>\n",
       "      <td>0.427273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.151904</td>\n",
       "      <td>0.953608</td>\n",
       "      <td>0.112884</td>\n",
       "      <td>0.121209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.435228</td>\n",
       "      <td>0.268943</td>\n",
       "      <td>0.265371</td>\n",
       "      <td>0.844899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.056052  0.270731  0.501479  0.121491\n",
       "1  0.770889  0.202760  0.180484  0.993555\n",
       "2  0.417544  0.766556  0.138167  0.899541\n",
       "3  0.040076  0.309443  0.443105  0.238431\n",
       "4  0.050177  0.261979  0.759423  0.372617\n",
       "5  0.834048  0.535997  0.057551  0.905784\n",
       "6  0.971209  0.889872  0.847377  0.687034\n",
       "7  0.167992  0.380885  0.561302  0.427273\n",
       "8  0.151904  0.953608  0.112884  0.121209\n",
       "9  0.435228  0.268943  0.265371  0.844899"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb5e1358>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD6CAYAAABamQdMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf07c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#选择第一行画柱状图\n",
    "df.iloc[0].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1a8438>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc200da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#直方图\n",
    "df = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),\n",
    "                   'c': np.random.randn(1000) - 1}, columns=list('abc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.902516</td>\n",
       "      <td>0.175676</td>\n",
       "      <td>1.483543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.478951</td>\n",
       "      <td>1.328754</td>\n",
       "      <td>-0.851907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.228000</td>\n",
       "      <td>0.245072</td>\n",
       "      <td>-1.666804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.671145</td>\n",
       "      <td>0.077473</td>\n",
       "      <td>-1.417179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.362950</td>\n",
       "      <td>2.218906</td>\n",
       "      <td>-1.709990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.737309</td>\n",
       "      <td>0.438842</td>\n",
       "      <td>0.228325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.797012</td>\n",
       "      <td>-1.099154</td>\n",
       "      <td>-0.959350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.171808</td>\n",
       "      <td>-0.860008</td>\n",
       "      <td>0.152194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.798960</td>\n",
       "      <td>1.207589</td>\n",
       "      <td>-1.897559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.246295</td>\n",
       "      <td>-0.527606</td>\n",
       "      <td>-1.686985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.176455</td>\n",
       "      <td>0.022385</td>\n",
       "      <td>-1.166454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.286695</td>\n",
       "      <td>-1.057058</td>\n",
       "      <td>-0.698135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.377497</td>\n",
       "      <td>-0.162845</td>\n",
       "      <td>-1.860837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.144571</td>\n",
       "      <td>-0.243156</td>\n",
       "      <td>-1.068962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.722664</td>\n",
       "      <td>1.718185</td>\n",
       "      <td>-1.613482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.653760</td>\n",
       "      <td>0.104450</td>\n",
       "      <td>-1.363017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.685858</td>\n",
       "      <td>-0.114899</td>\n",
       "      <td>-2.107467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.486439</td>\n",
       "      <td>-0.360206</td>\n",
       "      <td>-0.445543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.781253</td>\n",
       "      <td>0.283704</td>\n",
       "      <td>-0.150148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.800762</td>\n",
       "      <td>-2.064701</td>\n",
       "      <td>-1.511736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.286883</td>\n",
       "      <td>-0.780590</td>\n",
       "      <td>-1.500596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.195284</td>\n",
       "      <td>-0.752629</td>\n",
       "      <td>-0.923704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.742926</td>\n",
       "      <td>-0.042106</td>\n",
       "      <td>-3.756551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.455913</td>\n",
       "      <td>0.029757</td>\n",
       "      <td>-0.054625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.572510</td>\n",
       "      <td>-0.008689</td>\n",
       "      <td>0.342152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.985690</td>\n",
       "      <td>-2.318387</td>\n",
       "      <td>-0.477687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.929524</td>\n",
       "      <td>1.532749</td>\n",
       "      <td>0.655247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.447383</td>\n",
       "      <td>-2.219183</td>\n",
       "      <td>-0.534641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.191666</td>\n",
       "      <td>-0.297792</td>\n",
       "      <td>-0.011257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.354626</td>\n",
       "      <td>2.018627</td>\n",
       "      <td>-0.076992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0.407728</td>\n",
       "      <td>2.085965</td>\n",
       "      <td>-2.103196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>0.359136</td>\n",
       "      <td>2.733992</td>\n",
       "      <td>-1.320279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>0.417185</td>\n",
       "      <td>0.221353</td>\n",
       "      <td>-2.147104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>0.136460</td>\n",
       "      <td>-0.292955</td>\n",
       "      <td>-0.419654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>1.265829</td>\n",
       "      <td>-0.164987</td>\n",
       "      <td>-0.207629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>0.506537</td>\n",
       "      <td>0.175918</td>\n",
       "      <td>-2.613428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>-0.108547</td>\n",
       "      <td>-1.401323</td>\n",
       "      <td>-1.974330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>1.820644</td>\n",
       "      <td>0.290291</td>\n",
       "      <td>1.289072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2.967953</td>\n",
       "      <td>0.392798</td>\n",
       "      <td>-0.789165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>0.602913</td>\n",
       "      <td>2.204997</td>\n",
       "      <td>-0.019203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>-0.141408</td>\n",
       "      <td>-1.279475</td>\n",
       "      <td>0.609118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>0.606991</td>\n",
       "      <td>0.169109</td>\n",
       "      <td>-1.056976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2.789760</td>\n",
       "      <td>-0.939981</td>\n",
       "      <td>-1.040596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>0.739975</td>\n",
       "      <td>0.682669</td>\n",
       "      <td>-1.714606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>1.391463</td>\n",
       "      <td>-0.777806</td>\n",
       "      <td>-1.324724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>1.053105</td>\n",
       "      <td>-0.233702</td>\n",
       "      <td>-0.461921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>1.036363</td>\n",
       "      <td>0.360254</td>\n",
       "      <td>-1.510835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>0.358614</td>\n",
       "      <td>-1.459633</td>\n",
       "      <td>-0.746510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>-0.330285</td>\n",
       "      <td>-1.225116</td>\n",
       "      <td>-2.303197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>0.753958</td>\n",
       "      <td>-1.415398</td>\n",
       "      <td>-2.378918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>1.510093</td>\n",
       "      <td>-1.583183</td>\n",
       "      <td>-1.237777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>3.753660</td>\n",
       "      <td>-1.531003</td>\n",
       "      <td>-1.714068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>0.453235</td>\n",
       "      <td>0.584928</td>\n",
       "      <td>-1.399745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>0.795076</td>\n",
       "      <td>0.527118</td>\n",
       "      <td>-1.619733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>0.984648</td>\n",
       "      <td>0.594569</td>\n",
       "      <td>-0.245926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.396739</td>\n",
       "      <td>0.341086</td>\n",
       "      <td>-0.035345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.400282</td>\n",
       "      <td>-2.415888</td>\n",
       "      <td>-1.145578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.882134</td>\n",
       "      <td>-0.376131</td>\n",
       "      <td>0.343833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>0.960795</td>\n",
       "      <td>-0.174517</td>\n",
       "      <td>-0.803174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.342726</td>\n",
       "      <td>0.641715</td>\n",
       "      <td>-0.527754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            a         b         c\n",
       "0    0.902516  0.175676  1.483543\n",
       "1    0.478951  1.328754 -0.851907\n",
       "2    2.228000  0.245072 -1.666804\n",
       "3   -0.671145  0.077473 -1.417179\n",
       "4    2.362950  2.218906 -1.709990\n",
       "5    0.737309  0.438842  0.228325\n",
       "6    2.797012 -1.099154 -0.959350\n",
       "7   -0.171808 -0.860008  0.152194\n",
       "8   -0.798960  1.207589 -1.897559\n",
       "9    0.246295 -0.527606 -1.686985\n",
       "10   1.176455  0.022385 -1.166454\n",
       "11   0.286695 -1.057058 -0.698135\n",
       "12   2.377497 -0.162845 -1.860837\n",
       "13   0.144571 -0.243156 -1.068962\n",
       "14   1.722664  1.718185 -1.613482\n",
       "15   1.653760  0.104450 -1.363017\n",
       "16   0.685858 -0.114899 -2.107467\n",
       "17  -0.486439 -0.360206 -0.445543\n",
       "18   1.781253  0.283704 -0.150148\n",
       "19   0.800762 -2.064701 -1.511736\n",
       "20   2.286883 -0.780590 -1.500596\n",
       "21   0.195284 -0.752629 -0.923704\n",
       "22   0.742926 -0.042106 -3.756551\n",
       "23   0.455913  0.029757 -0.054625\n",
       "24   2.572510 -0.008689  0.342152\n",
       "25   0.985690 -2.318387 -0.477687\n",
       "26   0.929524  1.532749  0.655247\n",
       "27   0.447383 -2.219183 -0.534641\n",
       "28   1.191666 -0.297792 -0.011257\n",
       "29   1.354626  2.018627 -0.076992\n",
       "..        ...       ...       ...\n",
       "970  0.407728  2.085965 -2.103196\n",
       "971  0.359136  2.733992 -1.320279\n",
       "972  0.417185  0.221353 -2.147104\n",
       "973  0.136460 -0.292955 -0.419654\n",
       "974  1.265829 -0.164987 -0.207629\n",
       "975  0.506537  0.175918 -2.613428\n",
       "976 -0.108547 -1.401323 -1.974330\n",
       "977  1.820644  0.290291  1.289072\n",
       "978  2.967953  0.392798 -0.789165\n",
       "979  0.602913  2.204997 -0.019203\n",
       "980 -0.141408 -1.279475  0.609118\n",
       "981  0.606991  0.169109 -1.056976\n",
       "982  2.789760 -0.939981 -1.040596\n",
       "983  0.739975  0.682669 -1.714606\n",
       "984  1.391463 -0.777806 -1.324724\n",
       "985  1.053105 -0.233702 -0.461921\n",
       "986  1.036363  0.360254 -1.510835\n",
       "987  0.358614 -1.459633 -0.746510\n",
       "988 -0.330285 -1.225116 -2.303197\n",
       "989  0.753958 -1.415398 -2.378918\n",
       "990  1.510093 -1.583183 -1.237777\n",
       "991  3.753660 -1.531003 -1.714068\n",
       "992  0.453235  0.584928 -1.399745\n",
       "993  0.795076  0.527118 -1.619733\n",
       "994  0.984648  0.594569 -0.245926\n",
       "995  1.396739  0.341086 -0.035345\n",
       "996  0.400282 -2.415888 -1.145578\n",
       "997  2.882134 -0.376131  0.343833\n",
       "998  0.960795 -0.174517 -0.803174\n",
       "999  0.342726  0.641715 -0.527754\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6486d8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rp1gfM2+O1TLv2nbWaMKswTj2xfz8/IGqmu03rvPplUmmgN8HfrGqvpNkTY+rqn3APoDZ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\nopekxv0/bm+wQbbNCBsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5eed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['a'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D7A6EB8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D96FA20>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D92B080>], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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So18GrgS+mBX1Ad/LKygzMyteagvifOAc4FWAiNjGKFNtmJlZd0tNEHuyuZICQNKb8gvJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd620320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.hist(subplots=True, sharex=True, sharey=True)# 叠加stacked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdabcda0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZAJxS1dfcrmWCPgT8xu0iQigGmoKeNxMHf3gDRKQcqAZedbeSiHwN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\nxpiwLCyMMcaEZWFhjDEmrP8PTF5R3SX+Z3AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xda67a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 概率密度函数, 随机数服从正态分布\n",
    "df['a'].plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe630588>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E8FbfcB9NfU3WxhNOPWv03aduNF1F6dh01NBL7o8MjCQmKMZiUsiDzmpjMNwG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\nwczMci4KZmaW+wv5M/UDuTIn+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe63d780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.953547\n",
       "b   -0.019334\n",
       "c   -0.952817\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.012600\n",
       "b    1.005920\n",
       "c    1.017547\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot.scatter散点图, plot.pie饼图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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